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Course: RAG Evaluation Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to build the two foundational components of any RAG evaluation workflow: a golden dataset and a retrieval quality report. Without a golden dataset, no evaluation metric has meaning. Without retrieval evaluation, you cannot tell whether failures come from the retrieval stage or the generation stage. By completing this assignment, you will have a

Course: Docker for AI Apps Level: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to design and operate a multi-container Docker system for an AI application. You will configure container-to-container networking using a user-defined bridge network, orchestrate a multi-service stack with Docker Compose, build and containerize a FastAPI AI REST API with session management and health checks, apply Docker best practices incl

Course: Docker for AI Apps Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to work with Docker's core building blocks: running and inspecting containers, writing a production-ready Dockerfile, containerizing a Python AI application, and persisting data across container restarts using named volumes. By completing this assignment, you will have built and deployed a fully containerized multi-turn AI chatbot that retai

Course: RAG from Scratch Level: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to build the retrieval and generation stages of a RAG pipeline from scratch. You will implement cosine similarity without external vector search libraries, build a similarity search function, design a grounding-focused prompt template, and assemble a complete end-to-end RAG system that retrieves context and generates accurate, grounded answer

Course: RAG from Scratch Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to build the foundational stages of a RAG pipeline: loading documents, extracting clean text, attaching metadata, enriching documents with LLM-generated keywords, and splitting them into retrievable chunks. By completing this assignment, you will have built a reusable knowledge base preparation pipeline that you can apply to any document colle

Get expert help on 2026’s most in-demand skills including AI agents, LLM engineering, DevOps, and full-stack development. Codersarts offers assignment help, mentorship, and project support to help you build real-world applications faster.

Most RAG tutorials hide complexity. Learn RAG from scratch to build a complete system with ground-up understanding. Master chunking, embeddings, and prompt structure to build reliable, customizable RAG systems for private data. Develop the judgment that separates users from builders

When people talk about RAG systems, the conversation tends to focus on the same things: which LLM to use, how to write a better prompt, which vector database to choose, how to reduce API latency. Those are real concerns. But there is a quieter layer that gets overlooked almost every time. What happens to your documents before a query is ever asked? The answer to that question determines more about your system quality than almost any other decision. And yet it is the part of R

Most developers building with LLMs focus on the model. They tune prompts, swap models, and experiment with temperature settings. But the most common reason a RAG system gives a wrong answer has nothing to do with the LLM; it is because the right document was never retrieved in the first place. At CodersArts AI, our Hybrid Search and Re-Ranking: From Retrieval to Reliable Answers course is designed to help learners understand why pure vector search breaks and how to fix it u

When people talk about building AI systems today, the conversation usually revolves around: Which LLM to use How to write better prompts Which vector database is fastest Which framework to choose These are important decisions. But there’s a quieter layer in the stack that often gets overlooked — and yet, it has a disproportionate impact on system performance. That layer is chunking . What Is Chunking, Really? At a surface level, chunking is simple. You take a document and spl

RAG systems are the default architecture for AI applications, but they frequently fail, leading to incomplete answers, hallucinations, and missing context. The true, often overlooked, root cause of these issues is poor Chunking

Introduction Modern satellite constellations generate petabytes of multispectral, hyperspectral, SAR, and LiDAR data every day, far outpacing the capacity of traditional analysis methods. Remote sensing professionals must interpret this imagery against historical baselines, evolving scientific literature, environmental benchmarks, and mission-specific requirements simultaneously. Satellite Data Analysis Systems powered by Retrieval-Augmented Generation (RAG) address this by d

Introduction Loan underwriting requires the rapid processing of vast financial documents, regulatory guidelines, and market data under tight deadlines, a challenge that rigid scoring models and manual review workflows are ill-equipped to handle. Underwriters must assess creditworthiness, collateral quality, and compliance requirements while keeping pace with constantly shifting lending regulations and economic conditions. Loan Underwriting Systems powered by Retrieval-Augment

Introduction Veterinary professionals must deliver accurate diagnoses across many species with unique biological differences, while keeping up with constantly evolving research and treatment guidelines. Retrieval Augmented Generation powered diagnostic systems provide real time access to veterinary literature, species specific protocols, diagnostic data, and patient history. By retrieving and synthesizing the most relevant and up to date evidence, these systems deliver contex

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Here is a frustrating truth that most coding courses never tell you: you can finish a full curriculum, earn a certificate, and still feel completely lost when someone asks you to build a real product from scratch. The reason is simple. Learning to code and learning to build a product are two different skills — and the gap between them trips up thousands of developers every year. The students who close that gap fastest are the ones who learn by building something real, with st

Introduction Traditional AI interactions require rigid command structures limiting natural communication. Developers struggle to extract optimal responses from language models without specialized knowledge. Manual experimentation with different prompting approaches consumes significant development time. Inconsistent model outputs complicate production deployment and user experience. Llama 3:8B Chat transforms AI interactions through instruction-tuned conversational capabiliti

Introduction Traditional AI models process either text or images separately requiring multiple systems for comprehensive analysis. Businesses need solutions understanding visual content and answering questions about images. Manual image description and analysis consume significant time and resources. Language barriers complicate global image understanding applications. Gemma 3:4B Multimodal Model transforms visual understanding through combined vision and language processing.

Introduction In this comprehensive tutorial, we'll build a binary image classification system to detect fungal infections in microscopy images of vine wood. We'll use Vision Transformers (ViT), a state-of-the-art deep learning architecture that applies transformer concepts to image classification. Dataset Overview Dataset: "An Eye on the Vine" This dataset comes from research on pathogen segmentation in vinewood fluorescence microscopy images. The dataset is available at:...